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Open AccessFeature PaperArticle

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
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Remote Sens. 2019, 11(21), 2505; https://doi.org/10.3390/rs11212505
Received: 9 October 2019 / Revised: 22 October 2019 / Accepted: 23 October 2019 / Published: 25 October 2019
(This article belongs to the Special Issue Remote Sensing for Land Administration)
Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation. View Full-Text
Keywords: cadastral mapping; indirect surveying; boundary extraction; boundary delineation; machine learning; deep learning; image analysis; CNN; RF cadastral mapping; indirect surveying; boundary extraction; boundary delineation; machine learning; deep learning; image analysis; CNN; RF
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Crommelinck, S.; Koeva, M.; Yang, M.Y.; Vosselman, G. Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery. Remote Sens. 2019, 11, 2505.

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